a  d. @sJddlmZmZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z ddlmZddlmZeeedd d Zdeeed eeeeefd ddZd eeeed eeeedddZd!eeeed eeefdddZd"eeeeed eeeedddZd#eeedeeeeeed eeeed ddZdS)$)OptionalTupleN)Tensor)Literal)&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation _safe_divide)ClassificationTaskNoBinary)correcttotalreturncCs t||S)zReduce exact match.r )rrr{/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/classification/exact_match.py_exact_match_reduce srglobal)r samplewise)predstargetmultidim_average ignore_indexrcCsr|dur|}||||k<||kd|jdk}|dkr@|n|}tj|dkr^|jdnd|jd}||fS)Compute the statistics.Nrrrdevice)clonesumshapetorchtensorr)rrrrrrrrr_multiclass_exact_match_update(s "r$T)rr num_classesrr validate_argsrc CsXd\}}|r,t|||||t|||||t|||\}}t||||\}} t|| S)a Compute Exact match (also known as subset accuracy) for multiclass tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified. Accepts the following input tensors: - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing the number of labels multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``multidim_average`` argument: - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` Example (multidim tensors): >>> from torch import tensor >>> from torchmetrics.functional.classification import multiclass_exact_match >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='global') tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_exact_match >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='samplewise') tensor([1., 0.]) )rN)rrrr$r) rrr%rrr&Ztop_kaveragerrrrrmulticlass_exact_match9s:r()rr num_labelsrrcCsx|dkr4t|ddd|}t|ddd|}||kd|kjdd}tj|j|dkrbdnd|jd}||fS)rrr)Zdimrr)r"ZmovedimZreshaper r#r!r)rrr)rrrrrr_multilabel_exact_match_update|s "r,?)rrr) thresholdrrr&rc CsXd}|r(t|||||t|||||t|||||\}}t||||\}} t|| S)a Compute Exact match (also known as subset accuracy) for multilabel tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, C, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: The returned shape depends on the ``multidim_average`` argument: - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multilabel_exact_match >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_exact_match(preds, target, num_labels=3) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_exact_match >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_exact_match(preds, target, num_labels=3) tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_exact_match >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> multilabel_exact_match(preds, target, num_labels=3, multidim_average='samplewise') tensor([0., 0.]) N)r r r r,r) rrr)r.rrr&r'rrrrrmultilabel_exact_matchsDr/)Z multiclassZ multilabel) rrtaskr%r)r.rrr&rc Csnt|}|tjkr2|dus Jt||||||S|tjkr\|dusHJt|||||||Std|dS)aCompute Exact match (also known as subset accuracy). Exact Match is a stricter version of accuracy where all classes/labels have to match exactly for the sample to be correctly classified. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'multiclass'`` or ``'multilabel'``. See the documentation of :func:`~torchmetrics.functional.classification.multiclass_exact_match` and :func:`~torchmetrics.functional.classification.multilabel_exact_match` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='global') tensor(0.5000) >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise') tensor([1., 0.]) NzNot handled value: )rZfrom_strZ MULTICLASSr(Z MULTILABELr/ ValueError) rrr0r%r)r.rrr&rrr exact_matchs#     r2)rN)rNT)r)r-rNT)NNr-rNT)typingrrr"rZtyping_extensionsrZ2torchmetrics.functional.classification.stat_scoresrrrr r r Ztorchmetrics.utilities.computer Ztorchmetrics.utilities.enumsrrintr$boolr(r,floatr/r2rrrrs        D   Q